Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study

Preoperative Risk Assessment of Early Kidney Graft Loss

Background

A large proportion of potential organ donors are not utilized for kidney transplantation out of risk of early allograft loss because of donor-related characteristics. These can be summarized using kidney donor profile index (KDPI). Because KDPI affects the choice of the recipient, the predictive ability of KDPI is tied to recipient attributes. These have been questioned to explain most of the predictive ability of KDPI. This study aims to quantify the effect of the donor on early graft loss (EGL) by accounting for nonrandom allocation.

Methods

This study included patients undergoing kidney transplantation from deceased donors between 2014 and 2020 from the Scientific Registry of Transplantation Recipients. EGL, defined as a return to dialysis or retransplantation during the first posttransplant year, was the primary endpoint. Nonrandom allocation and donor-recipient matching by KDPI necessitated the use of inverse probability treatment weighting, which served to assess the effect of KDPI and mitigate selection bias in a weighted Cox regression model.

Results

The study comprised 89 290 transplantations in 88 720 individual patients. Inverse probability treatment weighting resulted in a good balance of recipient covariates across values of continuous KDPI. Weighted analysis showed KDPI to be a significant predictor for short-term outcomes. A comparable (in terms of age, time on dialysis, previous transplants, gender, diabetes status, computed panel-reactive antibodies, and HLA mismatches) average recipient, receiving a kidney from a donor with KDPI 40-60 had a 3.5% risk of EGL increased to a risk of 7.5% if received a kidney from a KDPI >95 donor (hazard ratio, 2.3; 95% confidence interval, 1.9-2.7). However, for all-cause survival KDPI was less influential.

Conclusions

The predictive ability of KDPI does not stem from recipient confounding alone. In this large sample-sized study, modeling methods accounting for nonindependence of recipient selection verify graft quality to effectively predict short-term transplantation outcomes.

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation

Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many years. Nevertheless, despite the question of whether AI models add value to traditional modeling approaches, such as regression models, their "black box" nature is one of the factors that have hindered the translation from research to clinical practice. Several techniques that make such models understandable to humans were developed with the promise of increasing transparency in the support of medical decision-making. These techniques should help AI to close the gap between theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulations. But is this also happening in the field of kidney transplantation? This review reports the use and explanation of "black box" models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other related outcomes after kidney transplantation. In particular, we emphasize the discussion on the need (or not) to explain ML models for biological discovery and clinical implementation in kidney transplantation. We also discuss promising future research paths for these computational tools.

Very Low Frequency of Pathological Findings in One-year Protocol Biopsies of Uneventful Standard Risk Kidney Transplant Recipients: Results From the Nordic Protocol Biopsy Study

Background

The clinical significance of kidney transplant protocol biopsies has been debated. We studied the frequency of borderline changes and T cell-mediated rejection (TCMR) in 1-y protocol biopsies in standard risk kidney transplant recipients.

Methods

Consecutive non-HLA-sensitized recipients of kidney transplants between 2006 and 2017, who underwent a protocol biopsy at 1 y in 2 national transplant centers were studied retrospectively (N = 1546). Donor-specific HLA antibodies (DSAs), graft function (plasma creatinine), and proteinuria were measured at the time of 1-y protocol biopsy. The occurrence of subclinical acute TCMR (i2t2v0 or higher) or borderline changes suspicious of TCMR (i1t1v0 or higher) in the protocol biopsy was studied, together with frequency of findings causing changes in the composite score iBox.

Results

Subclinical acute TCMR was detected in 30 of 1546 (1.9%) of the protocol biopsies, and borderline or TCMR in 179 of 1546 (12%). Among patients with no history of acute rejection, and no proteinuria or DSA, TCMR was detected in only 1 of 974 (0.1%) and borderline or TCMR in only 48 of 974 (4.9%) patients at 1 y. In the absence of proteinuria (<30 mg/g, or equivalent as measured with a negative dipstick proteinuria) or DSA, or history of acute rejection, only 50 of 974 (5.1%) biopsies showed any lesions significant for the iBox score.

Conclusions

The likelihood of pathological findings in 1-y protocol biopsies in non-HLA-sensitized patients without previous immunological events is low. Clinical usefulness of protocol biopsies seems limited in these patients.

Long-Term Outcomes after Conversion to a Belatacept-Based Immunosuppression in Kidney Transplant Recipients

BACKGROUND

Conversion to a belatacept-based immunosuppression is currently used as a calcineurin inhibitor (CNI) avoidance strategy when the CNI-based standard-of-care immunosuppression is not tolerated after kidney transplantation. However, there is a lack of evidence on the long-term benefit and safety after conversion to belatacept.

METHODS

We prospectively enrolled 311 kidney transplant recipients from 2007 to 2020 from two referral centers, converted from CNI to belatacept after transplant according to a prespecified protocol. Patients were matched at the time of conversion to patients maintained with CNIs, using optimal matching. The primary end point was death-censored allograft survival at 7 years. The secondary end points were patient survival, eGFR, and safety outcomes, including serious viral infections, immune-related complications, antibody-mediated rejection, T-cell-mediated rejection, de novo anti-HLA donor-specific antibody, de novo diabetes, cardiovascular events, and oncologic complications.

RESULTS

A total of 243 patients converted to belatacept (belatacept group) were matched to 243 patients maintained on CNIs (CNI control group). All recipient, transplant, functional, histologic, and immunologic parameters were well balanced between the two groups with a standardized mean difference below 0.05. At 7 years post-conversion to belatacept, allograft survival was 78% compared with 63% in the CNI control group ( P < 0.001 for log-rank test). The safety outcomes showed a similar rate of patient death (28% in the belatacept group versus 36% in the CNI control group), active antibody-mediated rejection (6% versus 7%), T-cell-mediated rejection (4% versus 4%), major adverse cardiovascular events, and cancer occurrence (9% versus 11%). A significantly higher rate of de novo proteinuria was observed in the belatacept group as compared with the CNI control group (37% versus 21%, P < 0.001).

CONCLUSIONS

This real-world evidence study shows that conversion to belatacept post-transplant was associated with lower risk of graft failure and acceptable safety outcomes compared with patients maintained on CNIs.

CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER

Long-term Outcomes after Conversion to Belatacept, NCT04733131 .

Comparing the prognostic performance of iBOX and biopsy-proven acute rejection for long-term kidney graft survival

Biopsy-proven acute rejection (BPAR) occurs in approximately 10% of kidney transplant recipients in the first year, making superiority trials unfeasible. iBOX, a quantitative composite of estimated glomerular filtration rate, proteinuria, antihuman leukocyte antigen donor-specific antibody, and + full/- abbreviated kidney histopathology, is a new proposed surrogate endpoint. BPAR's prognostic ability was compared with iBOX in a pooled cohort of 1534 kidney transplant recipients from 4 data sets, including 2 prospective randomized controlled trials. Discrimination analyses showed mean c-statistic differences between both iBOX compared with BPAR of 0.25 (95% confidence interval: 0.17-0.32) for full iBOX and 0.24 (95% confidence interval: 0.16-0.32) for abbreviated iBOX, indicating statistically significantly higher c-statistic values for the iBOX prognosis of death-censored graft survival. Mean (± standard error) c-statistics were 0.81 ± 0.03 for full iBOX, 0.80 ± 0.03 for abbreviated iBOX, and 0.57 ± 0.03 for BPAR. In calibration analyses, predicted graft loss events from both iBOX models were not significantly different from those observed. However, for BPAR, the predicted events were significantly (P < .01) different (observed: 64; predicted: 70; full iBOX: 76; abbreviated iBOX: 173 BPAR). IBOX at 1-year posttransplant is superior to BPAR in the first year posttransplant in graft loss prognostic performance, providing valuable additional information and facilitating the demonstration of superiority of novel immunosuppressive regimens.

The status and influencing factors of fatigue in kidney transplant recipients based on the theory of unpleasant symptoms: A cross-sectional study in China

AIMS

This study describes the incidence of fatigue in kidney transplant recipients and analyses the relationship between physiological factors, psychological factors, situational factors and fatigue in kidney transplant recipients.

BACKGROUND

Fatigue, as a common symptom after kidney transplantation, is affected by many factors, but the influence of some factors on the fatigue of kidney transplant recipients is still controversial.

DESIGN

This cross-sectional study was designed based on the theory of unpleasant symptoms.

METHODS

Our survey involved 307 participants attending the kidney transplant outpatient clinic of a tertiary Class A hospital (Changsha, Hunan, China). Data were collected between February and April 2021 using a structured questionnaire and electronic medical records. Data were analysed using IBM SPSS 25.0 (SPSS Inc.) RESULTS: It was found that the incidence of fatigue in kidney transplant recipients was 53.1%. According to the binary logistic regression analysis, sleep quality, hypokalemia, anxiety, depression and education level were independent risk factors for fatigue in kidney transplant recipients.

CONCLUSION

The incidence of fatigue in kidney transplant recipients was high and was influenced by physical, psychological and situational factors. Clinical nurses should assess fatigue levels in a timely and multidimensional manner in clinical practice and provide effective and scientific guidance about fatigue self-coping and symptom management for kidney transplant recipients.

Investigating the Impact of AI on Shared Decision-Making in Post-Kidney Transplant Care (PRIMA-AI): Protocol for a Randomized Controlled Trial

BACKGROUND

Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies. It is unknown whether the implementation of artificial intelligence (AI)-based risk prediction models can increase the number of conversations about treatment options after graft loss and how this might influence the associated shared decision-making (SDM).

OBJECTIVE

This study aims to explore the impact of AI-based risk prediction for the risk of graft loss on the frequency of conversations about the treatment options after graft loss, as well as the associated SDM process.

METHODS

This is a 2-year, prospective, randomized, 2-armed, parallel-group, single-center trial in a German kidney transplant center. All patients will receive the same routine post-kidney transplant care that usually includes follow-up visits every 3 months at the kidney transplant center. For patients in the intervention arm, physicians will be assisted by a validated and previously published AI-based risk prediction system that estimates the risk for graft loss in the next year, starting from 3 months after randomization until 24 months after randomization. The study population will consist of 122 kidney transplant recipients >12 months after transplantation, who are at least 18 years of age, are able to communicate in German, and have an estimated glomerular filtration rate <30 mL/min/1.73 m2. Patients with multi-organ transplantation, or who are not able to communicate in German, as well as underage patients, cannot participate. For the primary end point, the proportion of patients who have had a conversation about their treatment options after graft loss is compared at 12 months after randomization. Additionally, 2 different assessment tools for SDM, the CollaboRATE mean score and the Control Preference Scale, are compared between the 2 groups at 12 months and 24 months after randomization. Furthermore, recordings of patient-physician conversations, as well as semistructured interviews with patients, support persons, and physicians, are performed to support the quantitative results.

RESULTS

The enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025.

CONCLUSIONS

This is the first study to examine the influence of AI-based risk prediction on physician-patient interaction in the context of kidney transplantation. We use a mixed methods approach by combining a randomized design with a simple quantitative end point (frequency of conversations), different quantitative measurements for SDM, and several qualitative research methods (eg, records of physician-patient conversations and semistructured interviews) to examine the implementation of AI-based risk prediction in the clinic.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54857.

Efficacy and Safety of Bleselumab in Preventing the Recurrence of Primary Focal Segmental Glomerulosclerosis in Kidney Transplant Recipients: A Phase 2a, Randomized, Multicenter Study

BACKGROUND

Focal segmental glomerulosclerosis (FSGS) is a common cause of end-stage kidney disease and frequently recurs after kidney transplantation. Recurrent FSGS (rFSGS) is associated with poor allograft and patient outcomes. Bleselumab, a fully human immunoglobulin G4 anti-CD40 antagonistic monoclonal antibody, disrupts CD40-related processes in FSGS, potentially preventing rFSGS.

METHODS

A phase 2a, randomized, multicenter, open-label study of adult recipients (aged ≥18 y) of a living or deceased donor kidney transplant with a history of biopsy-proven primary FSGS. The study assessed the efficacy of bleselumab combined with tacrolimus and corticosteroids as maintenance immunosuppression in the prevention of rFSGS >12 mo posttransplantation, versus standard of care (SOC) comprising tacrolimus, mycophenolate mofetil, and corticosteroids. All patients received basiliximab induction. The primary endpoint was rFSGS, defined as proteinuria (protein-creatinine ratio ≥3.0 g/g) with death, graft loss, or loss to follow-up imputed as rFSGS, through 3 mo posttransplant.

RESULTS

Sixty-three patients were followed for 12 mo posttransplantation. Relative decrease in rFSGS occurrence through 3 mo with bleselumab versus SOC was 40.7% (95% confidence interval, -89.8 to 26.8; P = 0.37; absolute decrease 12.7% [95% confidence interval, -34.5 to 9.0]). Central-blinded biopsy review found relative (absolute) decreases in rFSGS of 10.9% (3.9%), 17.0% (6.2%), and 20.5% (7.5%) at 3, 6, and 12 mo posttransplant, respectively; these differences were not statistically significant. Adverse events were similar for both treatments. No deaths occurred during the study.

CONCLUSIONS

In at-risk kidney transplant recipients, bleselumab numerically reduced proteinuria occurrence versus SOC, but no notable difference in occurrence of biopsy-proven rFSGS was observed.

Tocilizumab in chronic active antibody-mediated rejection: rationale and protocol of an in-progress randomized controlled open-label multi-center trial (INTERCEPT study)

BACKGROUND

Chronic active antibody-mediated rejection (caAMR) in kidney transplants is associated with irreversible tissue damage and a leading cause of graft loss in the long-term. However, the treatment for caAMR remains a challenge to date. Recently, tocilizumab, a recombinant humanized monoclonal antibody directed against the human interleukin-6 (IL-6) receptor, has shown promise in the treatment of caAMR. However, it has not been systematically investigated so far underscoring the need for randomized controlled studies in this area.

METHODS

The INTERCEPT study is an investigator-driven randomized controlled open-label multi-center trial in kidney transplant recipients to assess the efficacy of tocilizumab in the treatment of biopsy-proven caAMR. A total of 50 recipients with biopsy-proven caAMR at least 12 months after transplantation will be randomized to receive either tocilizumab (n = 25) added to our standard of care (SOC) maintenance treatment or SOC alone (n = 25) for a period of 24 months. Patients will be followed for an additional 12 months after cessation of study medication. After the inclusion biopsies at baseline, protocol kidney graft biopsies will be performed at 12 and 24 months. The sample size calculation assumed a difference of 5 ml/year in slope of estimated glomerular filtration rate (eGFR) between the two groups for 80% power at an alpha of 0.05. The primary endpoint is the slope of eGFR at 24 months after start of treatment. The secondary endpoints include assessment of the following at 12, 24, and 36 months: composite risk score iBox, safety, evolution and characteristics of donor-specific antibodies (DSA), graft histology, proteinuria, kidney function assessed by measured GFR (mGFR), patient- and death-censored graft survival, and patient-reported outcomes that include transplant-specific well-being, adherence to immunosuppressive medications and perceived threat of the risk of graft rejection.

DISCUSSION

No effective treatment exists for caAMR at present. Based on the hypothesis that inhibition of IL-6 receptor by tocilizumab will reduce antibody production and reduce antibody-mediated damage, our randomized trial has a potential to provide evidence for a novel treatment strategy for caAMR, therewith slowing the decline in graft function in the long-term.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04561986. Registered on September 24, 2020.

The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts

The XVIth Banff Meeting for Allograft Pathology was held in Banff, Alberta, Canada, from September 19 to 23, 2022, as a joint meeting with the Canadian Society of Transplantation. In addition to a key focus on the impact of microvascular inflammation and biopsy-based transcript analysis on the Banff Classification, further sessions were devoted to other aspects of kidney transplant pathology, in particular T cell-mediated rejection, activity and chronicity indices, digital pathology, xenotransplantation, clinical trials, and surrogate endpoints. Although the output of these sessions has not led to any changes in the classification, the key role of Banff Working Groups in phrasing unanswered questions, and coordinating and disseminating results of investigations addressing these unanswered questions was emphasized. This paper summarizes the key Banff Meeting 2022 sessions not covered in the Banff Kidney Meeting 2022 Report paper and also provides an update on other Banff Working Group activities relevant to kidney allografts.

Prognostic Biomarkers in Kidney Transplantation: A Systematic Review and Critical Appraisal

SIGNIFICANCE STATEMENT

Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice.

BACKGROUND

Despite the increasing number of biomarker studies published in the transplant literature over the past 20 years, demonstrations of their clinical benefit and their implementation in routine clinical practice are lacking. We hypothesized that suboptimal design, data, methodology, and reporting might contribute to this phenomenon.

METHODS

We formed a consortium of experts in systematic reviews, nephrologists, methodologists, and epidemiologists. A systematic literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library between January 1, 2005, and November 12, 2022 (PROSPERO ID: CRD42020154747). All English language, original studies investigating the association between a biomarker and kidney allograft outcome were included. The final set of publications was assessed by expert reviewers. After data collection, two independent reviewers randomly evaluated the inconsistencies for 30% of the references for each reviewer. If more than 5% of inconsistencies were observed for one given reviewer, a re-evaluation was conducted for all the references of the reviewer. The biomarkers were categorized according to their type and the biological milieu from which they were measured. The study characteristics related to the design, methods, results, and their interpretation were assessed, as well as reproducible research practices and transparency indicators.

RESULTS

A total of 7372 publications were screened and 804 studies met the inclusion criteria. A total of 1143 biomarkers were assessed among the included studies from blood ( n =821, 71.8%), intragraft ( n =169, 14.8%), or urine ( n =81, 7.1%) compartments. The number of studies significantly increased, with a median, yearly number of 31.5 studies (interquartile range [IQR], 23.8-35.5) between 2005 and 2012 and 57.5 (IQR, 53.3-59.8) between 2013 and 2022 ( P < 0.001). A total of 655 studies (81.5%) were retrospective, while 595 (74.0%) used data from a single center. The median number of patients included was 232 (IQR, 96-629) with a median follow-up post-transplant of 4.8 years (IQR, 3.0-6.2). Only 4.7% of studies were externally validated. A total of 346 studies (43.0%) did not adjust their biomarker for key prognostic factors, while only 3.1% of studies adjusted the biomarker for standard-of-care patient monitoring factors. Data sharing, code sharing, and registration occurred in 8.8%, 1.1%, and 4.6% of studies, respectively. A total of 158 studies (20.0%) emphasized the clinical relevance of the biomarker, despite the reported nonsignificant association of the biomarker with the outcome measure. A total of 288 studies assessed rejection as an outcome. We showed that these rejection studies shared the same characteristics as other studies.

CONCLUSIONS

Biomarker studies in kidney transplantation lack validation, rigorous design and methodology, accurate interpretation, and transparency. Higher standards are needed in biomarker research to prove the clinical utility and support clinical use.

Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories

Background

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival.

Methods

First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed.

Results

The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found.

Conclusions

This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.

Creatinine-cystatin C ratio and death with a functioning graft in kidney transplant recipients

Death with a functioning graft is important cause of graft loss after kidney transplantation. However, little is known about factors predicting death with a functioning graft among kidney transplant recipients. In this study, we evaluated the association between post-transplant creatinine-cystatin C ratio and death with a functioning graft in 1592 kidney transplant recipients. We divided the patients into tertiles based on sex-specific creatinine-cystatin C ratio. Among the 1592 recipients, 39.5% were female, and 86.1% underwent living-donor kidney transplantation. The cut-off value for the lowest creatinine-cystatin C ratio tertile was 0.86 in males and 0.73 in females. The lowest tertile had a significantly lower 5-year patient survival rate and was independently associated with death with a functioning graft (adjusted hazard ratio 2.574, 95% confidence interval 1.339-4.950, P < 0.001). Infection was the most common cause of death in the lowest tertile group, accounting for 62% of deaths. A low creatinine-cystatin C ratio was significantly associated with an increased risk of death with a functioning graft after kidney transplantation.

The power of partnership: Exploring collaboration dynamics in U.S. transplant research

INTRODUCTION

Collaboration is one of the hallmarks of academic research. This study analyzes collaboration patterns in U.S. transplant research, examining publication trends, productive institutions, co-authorship networks, and citation patterns in high-impact transplant journals.

METHODS

4,265 articles published between 2012 and 2021 were analyzed using scientometric tools, logistic regression, VantagePoint software, and Gephi software for network visualization.

RESULTS

16,003 authors from 1,011 institutions and 59 countries were identified, with Harvard, Johns Hopkins, and University of Pennsylvania contributing the most papers. Odds of international collaboration significantly increased over time (OR 1.03; p ​= ​0.040), while odds of citation in single-institution collaborations decreased (OR 0.99; p ​= ​0.016). Five major scientific communities and central institutions (Harvard University and University of Pittsburgh) connecting them were identified, revealing interconnected research clusters.

CONCLUSIONS

Collaboration enhances knowledge exchange and research productivity, with an increasing trend of institutional and international collaboration in U.S. transplant research. Understanding this community is essential for promoting research impact and forming strategic partnerships.

Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review

Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.

NephroCAGE-German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

BACKGROUND

Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures.

OBJECTIVE

The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models.

METHODS

To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients.

RESULTS

We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023.

CONCLUSIONS

For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48892.

Early kinetics of donor-derived cell-free DNA after transplantation predicts renal graft recovery and long-term function

BACKGROUND

Ischemia-reperfusion injury (IRI) upon transplantation is one of the most impactful events that the kidney graft suffers during its life. Its clinical manifestation in the recipient, delayed graft function (DGF), has serious prognostic consequences. However, the different definitions of DGF are subject to physicians' choices and centers' policies, and a more objective tool to quantify IRI is needed. Here, we propose the use of donor-derived cell-free DNA (ddcfDNA) for this scope.

METHODS

ddcfDNA was assessed in 61 kidney transplant recipients of either living or deceased donors at 24 h, and 7, 14 and 30 days after transplantation using the AlloSeq cfDNA Kit (CareDx, San Francisco, CA, USA). Patients were followed-up for 6 months and 7-year graft survival was estimated through the complete and functional iBox tool.

RESULTS

Twenty-four-hour ddcfDNA was associated with functional DGF [7.20% (2.35%-15.50%) in patients with functional DGF versus 2.70% (1.55%-4.05%) in patients without it, P = .023] and 6-month estimated glomerular filtration rate (r = -0.311, P = .023). At Day 7 after transplantation, ddcfDNA was associated with dialysis duration in DGF patients (r = 0.612, P = .005) and worse 7-year iBox-estimated graft survival probability (β -0.42, P = .001) at multivariable analysis. Patients with early normalization of ddcfDNA (<0.5% at 1 week) had improved functional iBox-estimated probability of graft survival (79.5 ± 16.8%) in comparison with patients with 7-day ddcfDNA ≥0.5% (67.7 ± 24.1%) (P = .047).

CONCLUSIONS

ddcfDNA early kinetics after transplantation reflect recovery from IRI and are associated with short-, medium- and long-term graft outcome. This may provide a more objective estimate of IRI severity in comparison with the clinical-based definitions of DGF.

Predictors of graft failure after first detection of de novo donor-specific HLA antibodies in kidney transplant recipients

BACKGROUND

De novo donor-specific antibodies (dnDSAs) may cause antibody-mediated rejection and graft dysfunction. Little is known about the clinical course after first detection of dnDSAs during screening in asymptomatic patients. We aimed to assess the value of estimated glomerular filtration rate (eGFR) and proteinuria to predict graft failure in patients with dnDSAs and their potential utility as surrogate endpoints.

METHODS

All 400 kidney transplant recipients with dnDSAs at our centre (1 March 2000-31 May 2021) were included in this retrospective study. The dates of graft loss, rejection, doubling of creatinine, ≥30% eGFR decline, proteinuria ≥500 mg/g and ≥1000 mg/g were registered from the first dnDSA appearance.

RESULTS

During 8.3 years of follow-up, graft failure occurred in 33.3% of patients. Baseline eGFR and proteinuria correlated with 5-year graft loss (area under the receiver operating characteristics curve 0.75 and 0.80, P < .001). Creatinine doubled after a median of 2.8 years [interquartile range (IQR) 1.5-5.0] from dnDSA and the time from doubling creatinine to graft failure was 1.0 year (IQR 0.4-2.9). Analysing eGFR reduction ≥30% as a surrogate endpoint (148/400), the time from dnDSA to this event was 2.0 years (IQR 0.6-4.2), with a positive predictive value (PPV) of 45.9% to predict graft loss, which occurred after 2.0 years (IQR 0.8-3.2). The median time from proteinuria ≥500 mg/g and ≥1000 mg/g to graft failure was identical, 1.8 years, with a PPV of 43.8% and 49.0%, respectively. Composite endpoints did not improve PPV. Multivariable analysis showed that rejection was the most important independent risk factor for all renal endpoints and graft loss.

CONCLUSIONS

Renal function, proteinuria and rejection are strongly associated with graft failure in patients with dnDSA and may serve as surrogate endpoints.

Complement and Non-Complement Binding Anti-HLA Antibodies Are Differentially Detected with Different Antigen Bead Assays in Renal Transplant Recipients

Two semi-quantitative, Luminex-based, single-antigen bead (SAB) assays are available to detect anti-HLA antibodies and evaluate their reactivity with complement binding. Sera from 97 patients with positive panel reactive antibody tests (>5%) were analyzed with two SAB tests, Immucor (IC) and One-Lambda (OL), for anti-HLA antibody detection and the evaluation of their complement-binding capacity. IC detected 1608/8148 (mean fluorescent intensity (MFI) 4195 (1995-11,272)) and 1136/7275 (MFI 6706 (2647-13,184)) positive anti-HLA class I and II specificities, respectively. Accordingly, OL detected 1942/8148 (MFI 6185 (2855-12,099)) and 1247/7275 (MFI 9498 (3630-17,702)) positive anti-HLA class I and II specificities, respectively. For the IC assay, 428/1608 (MFI 13,900 (9540-17,999)) and 409/1136 (MFI 11,832 (7128-16,531)) positive class I and II specificities bound C3d, respectively. Similarly, OL detected 485/1942 (MFI 15,452 (9369-23,095)) and 298/1247 (MFI18,852 (14,415-24,707)) C1q-binding class I and II specificities. OL was more sensitive in detecting class I and II anti-HLA antibodies than IC was, although there was no significant difference in the number of class II specificities per case. MFI was higher for complement vs. non-complement-binding anti-HLA antibodies in both assays. Both methods were equal in detecting complement-binding anti-HLA class I antibodies, whereas the C3d assay was more sensitive in detecting complement-binding anti-HLA class II antibodies.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence

The ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.

Multi-phasic eGFR trajectory during follow up and long-term graft failure after kidney transplantation

BACKGROUND

The prevailing assumption is that following kidney transplantation the pattern of kidney function decline is consistent. Nevertheless, numerous factors leading to graft loss may emerge, altering the trajectory of kidney function. In this study, we aim to assess alterations in estimated glomerular filtration rate (eGFR) trajectory over an extended period of follow-up and examine its correlation with graft survival.

METHODS

We calculated eGFR using all creatinine values available from 1-year post transplantation to the end of follow-up. For pattern analysis, we used a piecewise linear model.

RESULTS

Nine hundred eighty-eight patients were included in the study. After a median follow-up of 5.2 years, 297 (30.1%) patients had a multi-phasic eGFR trajectory. Change in eGFR trajectory was associated with increased risk for graft failure (HR 7.15, 95% CI 5.17-9.89, p < .001), longer follow-up time, younger age, longer cold ischemia time, high prevalence of acute rejection, longer hospitalization and a lower initial eGFR. Of the 988 patients included in the study, 494 (50.0%) had a mono-phasic stable trajectory, 197 (19.9%) had a mono-phasic decreasing trajectory, 184 (18.6%) had bi-phasic decreasing trajectory (initial stability and then decline, 46(4.7%) had a bi-phasic stabilized (initial decline and then stabilization) and 67(6.8%) had a more complex trajectory (tri-phasic). Out of the total 144 patients who experienced graft loss, the predominant pattern was a bi-phasic decline characterized by a bi-linear trajectory (66 events, 45.8%).

CONCLUSIONS

Changes in eGFR trajectory during long-term follow-up can serve as a valuable tool for assessing the underlying mechanisms contributing to graft loss.

Challenges in the management of the kidney allograft: from decline to failure: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

In March 2022, Kidney Disease: Improving Global Outcomes (KDIGO) held a virtual Controversies Conference to address the important but rarely examined phase during which the kidney transplant is failing or has failed. In addition to discussing the definition of a failing allograft, 4 broad areas were considered in the context of a declining functioning graft: prognosis and kidney failure trajectory; immunosuppression strategies; management of medical and psychological complications, and patient factors; and choice of kidney replacement therapy or supportive care following graft loss. Identifying and paying special attention to individuals with failing allografts was felt to be important in order to prepare patients psychologically, manage immunosuppression, address complications, prepare for dialysis and/or retransplantation, and transition to supportive care. Accurate prognostication tools, although not yet widely available, were embraced as necessary to define allograft survival trajectories and the likelihood of allograft failure. The decision of whether to withdraw or continue immunosuppression after allograft failure was deemed to be based most appropriately on risk-benefit analysis and likelihood of retransplantation within a few months. Psychological preparation and support was identified as a critical factor in patient adjustment to graft failure, as was early communication. Several models of care were noted that enabled a medically supportive transition back to dialysis or retransplantation. Emphasis was placed on the importance of dialysis-access readiness before initiation of dialysis, in order to avoid use of central venous catheters. The centrality of the patient to all management decisions and discussions was deemed to be paramount. Patient "activation," which can be defined as engaged agency, was seen as the most effective way to achieve success. Unresolved controversies, gaps in knowledge, and areas for research were also stressed in the conference deliberations.

Plasma protein signatures reflect systemic immunity and allograft function in kidney transplantation

Kidney transplantation causes large perturbations of the immune system. While many studies focus on the allograft, insights into systemic effects are largely missing. Here, we analyzed the systemic immune response in 3 cohorts of kidney transplanted patients. Using serum proteomics, laboratory values, mass cytometry, histological and clinical parameters, inter-patient heterogeneity was leveraged for multi-omic co-variation analysis. We identified circulating immune modules (CIM) that describe extra-renal signatures of co-regulated plasma proteins. CIM are present in nontransplanted controls, in transplant conditions and during rejection. They are enriched in pathways linked to kidney function, extracellular matrix, signaling, and cellular activation. A complex leukocyte response in the blood during allograft quiescence and rejection is associated with CIM activity and CIM-specific cytokines. CIM activity correlates with kidney function including a 2-month prediction. Together, the data suggest a systemic and multi-layered response of transplant immunity that might be insightful for understanding allograft dysfunction and developing translational biomarkers.

Artificial intelligence: a new field of knowledge for nephrologists?

Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

Immune monitoring of allograft status in kidney transplant recipients

Kidney transplant patients require careful management of immunosuppression to avoid rejection while minimizing the risk of infection and malignancy for the best long-term outcome. The gold standard for monitoring allograft status and immunosuppression adequacy is a kidney biopsy, but this is invasive and costly. Conventional methods of allograft monitoring, such as serum creatinine level, are non-specific. Although they alert physicians to the need to evaluate graft dysfunction, by the time there is a clinical abnormality, allograft damage may have already occurred. The development of novel and non-invasive methods of evaluating allograft status are important to improving graft outcomes. This review summarizes the available conventional and novel methods for monitoring allograft status after kidney transplant. Novel and less invasive methods include gene expression, cell-free DNA, urinary biomarkers, and the use of artificial intelligence. The optimal method to manage patients after kidney transplant is still being investigated. The development of less invasive methods to assess allograft function has the potential to improve patient outcomes and allow for a more personalized approach to immunosuppression management.

Donor-Derived Cell-Free DNA (dd-cfDNA) in Kidney Transplant Recipients With Indication Biopsy-Results of a Prospective Single-Center Trial

Donor-derived cell-free DNA (dd-cfDNA) identifies allograft injury and discriminates active rejection from no rejection. In this prospective study, 106 kidney transplant recipients with 108 clinically indicated biopsies were enrolled at Heidelberg University Hospital between November 2020 and December 2022 to validate the clinical value of dd-cfDNA in a cohort of German patients. dd-cfDNA was quantified at biopsy and correlated to histopathology. Additionally, dd-cfDNA was determined on days 7, 30, and 90 post-biopsy and analyzed for potential use to monitor response to anti-rejection treatment. dd-cfDNA levels were with a median (IQR) % of 2.00 (0.48-3.20) highest in patients with ABMR, followed by 0.92 (0.19-11.25) in patients with TCMR, 0.44 (0.20-1.10) in patients with borderline changes and 0.20 (0.11-0.53) in patients with no signs of rejection. The AUC for dd-cfDNA to discriminate any type of rejection including borderline changes from no rejection was at 0.72 (95% CI 0.62-0.83). In patients receiving anti-rejection treatment, dd-cfDNA levels significantly decreased during the 7, 30, and 90 days follow-up compared to levels at the time of biopsy (p = 0.006, p = 0.002, and p < 0.001, respectively). In conclusion, dd-cfDNA significantly discriminates active rejection from no rejection. Decreasing dd-cfDNA following anti-rejection treatment may indicate response to therapy. Clinical Trial Registration: https://drks.de/search/de/trial/DRKS00023604, identifier DRKS00023604.

Systemic inflammation early after kidney transplantation is associated with long-term graft loss: a cohort study

Background

Early graft loss following kidney transplantation is mainly a result of acute rejection or surgical complications, while long-term kidney allograft loss is more complex. We examined the association between systemic inflammation early after kidney transplantation and long-term graft loss, as well as correlations between systemic inflammation scores and inflammatory findings in biopsies 6 weeks and 1 year after kidney transplantation.

Methods

We measured 21 inflammatory biomarkers 10 weeks after transplantation in 699 patients who were transplanted between 2009 and 2012 at Oslo University Hospital, Rikshospitalet, Norway. Low-grade inflammation was assessed with predefined inflammation scores based on specific biomarkers: one overall inflammation score and five pathway-specific scores. Surveillance or indication biopsies were performed in all patients 6 weeks after transplantation. The scores were tested in Cox regression models.

Results

Median follow-up time was 9.1 years (interquartile range 7.6-10.7 years). During the study period, there were 84 (12.2%) death-censored graft losses. The overall inflammation score was associated with long-term kidney graft loss both when assessed as a continuous variable (hazard ratio 1.03, 95% CI 1.01-1.06, P = 0.005) and as a categorical variable (4th quartile: hazard ratio 3.19, 95% CI 1.43-7.10, P = 0.005). In the pathway-specific analyses, fibrogenesis activity and vascular inflammation stood out. The vascular inflammation score was associated with inflammation in biopsies 6 weeks and 1 year after transplantation, while the fibrinogenesis score was associated with interstitial fibrosis and tubular atrophy.

Conclusion

In conclusion, a systemic inflammatory environment early after kidney transplantation was associated with biopsy-confirmed kidney graft pathology and long-term kidney graft loss. The systemic vascular inflammation score correlated with inflammatory findings in biopsies 6 weeks and 1 year after transplantation.

Validation of a prediction system for risk of kidney allograft failure in pediatric kidney transplant recipients: An international observational study

Predicting long-term kidney allograft failure is an unmet need for clinical care and clinical trial optimization in children. We aimed to validate a kidney allograft failure risk prediction system in a large international cohort of pediatric kidney transplant recipients. Patients from 20 centers in Europe and the United States, transplanted between 2004 and 2017, were included. Allograft assessment included estimated glomerular filtration rate, urine protein-to-creatinine ratio, circulating antihuman leukocyte antigen donor-specific antibody, and kidney allograft histology. Individual predictions of allograft failure were calculated using the integrative box (iBox) system. Prediction performances were assessed using discrimination and calibration. The allograft evaluations were performed in 706 kidney transplant recipients at a median time of 9.1 (interquartile range, 3.3-19.2) months posttransplant; mean estimated glomerular filtration rate was 68.7 ± 28.1 mL/min/1.73 m2, and median urine protein-to-creatinine ratio was 0.1 (0.0-0.4) g/g, and 134 (19.0%) patients had antihuman leukocyte antigen donor-specific antibodies. The iBox exhibited accurate calibration and discrimination for predicting the outcomes up to 10 years after evaluation, with a C-index of 0.81 (95% confidence interval, 0.75-0.87). This study confirms the generalizability of the iBox to predict long-term kidney allograft failure in children, with performances similar to those reported in adults. These results support the use of the iBox to improve patient monitoring and facilitate clinical trials in children.

Qualifying a novel clinical trial endpoint (iBOX) predictive of long-term kidney transplant outcomes

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Qualifying a Novel Clinical Trial Endpoint (iBOX) Predictive of Long-Term Kidney Transplant Outcomes

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

A Multi-Step Precision Pathway for Predicting Allograft Survival in Heterogeneous Cohorts of Kidney Transplant Recipients

Accurate prediction of allograft survival after kidney transplantation allows early identification of at-risk recipients for adverse outcomes and initiation of preventive interventions to optimize post-transplant care. Many prediction algorithms do not model cohort heterogeneity and may lead to inaccurate assessment of longer-term graft outcomes among minority groups. Using data from a national Australian kidney transplant cohort (2008-2017) as the derivation set, we developed P-Cube, a multi-step precision prediction pathway model for predicting overall graft survival in three ethnic subgroups: European Australians, Asian Australians and Aboriginal and Torres Strait Islander Peoples. The concordance index for the European Australians, Asian Australians, and Aboriginal and Torres Strait Islander Peoples subpopulations were 0.99 (0.98-0.99), 0.93 (0.92-0.94) and 0.92 (0.91-0.93), respectively. Similar findings were observed when validating P-cube using an external dataset [Scientific Registry of Transplant Recipient Registry (2006-2020)]. Six sub-categories of recipients with distinct risk factor profiles were identified. Some factors such as blood group compatibility were considered important across the entire transplant population. Other factors such as human leukocyte antigen (HLA)-DR mismatches were unique to older recipients. The P-cube model identifies allograft survival specific risk factors within a heterogenous population and offers personalized survival predictions in a diverse cohort.

Longitudinal estimated glomerular filtration rate (eGFR) modeling in long-term renal function to inform clinical trial design in kidney transplantation

Kidney transplantation is the preferred treatment for individuals with end-stage kidney disease. From a modeling perspective, our understanding of kidney function trajectories after transplantation remains limited. Current modeling of kidney function post-transplantation is focused on linear slopes or percent decline and often excludes the highly variable early timepoints post-transplantation, where kidney function recovers and then stabilizes. Using estimated glomerular filtration rate (eGFR), a well-known biomarker of kidney function, from an aggregated dataset of 4904 kidney transplant patients including both observational studies and clinical trials, we developed a longitudinal model of kidney function trajectories from time of transplant to 6 years post-transplant. Our model is a nonlinear, mixed-effects model built in NONMEM that captured both the recovery phase after kidney transplantation, where the graft recovers function, and the long-term phase of stabilization and slow decline. Model fit was assessed using diagnostic plots and individual fits. Model performance, assessed via visual predictive checks, suggests accurate model predictions of eGFR at the median and lower 95% quantiles of eGFR, ranges which are of critical clinical importance for assessing loss of kidney function. Various clinically relevant covariates were also explored and found to improve the model. For example, transplant recipients of deceased donors recover function more slowly after transplantation and calcineurin inhibitor use promotes faster long-term decay. Our work provides a generalizable, nonlinear model of kidney allograft function that will be useful for estimating eGFR up to 6 years post-transplant in various clinically relevant populations.

Molecular immune monitoring in kidney transplant rejection: a state-of-the-art review

Although current regimens of immunosuppressive drugs are effective in renal transplant recipients, long-term renal allograft outcomes remain suboptimal. For many years, the diagnosis of renal allograft rejection and of several causes of renal allograft dysfunction, such as chronic subclinical inflammation and infection, was mostly based on renal allograft biopsy, which is not only invasive but also possibly performed too late for proper management. In addition, certain allograft dysfunctions are difficult to differentiate from renal histology due to their similar pathogenesis and immune responses. As such, non-invasive assays and biomarkers may be more beneficial than conventional renal biopsy for enhancing graft survival and optimizing immunosuppressive drug regimens during long-term care. This paper discusses recent biomarker candidates, including donor-derived cell-free DNA, transcriptomics, microRNAs, exosomes (or other extracellular vesicles), urine chemokines, and nucleosomes, that show high potential for clinical use in determining the prognosis of long-term outcomes of kidney transplantation, along with their limitations.

Regulatory T Cell Biomarkers Identify Patients at Risk of Developing Acute Cellular Rejection in the First Year Following Heart Transplantation

BACKGROUND

Acute cellular rejection (ACR), an alloimmune response involving CD4+ and CD8+ T cells, occurs in up to 20% of patients within the first year following heart transplantation. The balance between a conventional versus regulatory CD4+ T cell alloimmune response is believed to contribute to developing ACR. Therefore, tracking these cells may elucidate whether changes in these cell populations could signal ACR risk.

METHODS

We used a CD4+ T cell gene signature (TGS) panel that tracks CD4+ conventional T cells (Tconv) and regulatory T cells (Treg) on longitudinal samples from 94 adult heart transplant recipients. We evaluated combined diagnostic performance of the TGS panel with a previously developed biomarker panel for ACR diagnosis, HEARTBiT, while also investigating TGS' prognostic utility.

RESULTS

Compared with nonrejection samples, rejection samples showed decreased Treg- and increased Tconv-gene expression. The TGS panel was able to discriminate between ACR and nonrejection samples and, when combined with HEARTBiT, showed improved specificity compared with either model alone. Furthermore, the increased risk of ACR in the TGS model was associated with lower expression of Treg genes in patients who later developed ACR. Reduced Treg gene expression was positively associated with younger recipient age and higher intrapatient tacrolimus variability.

CONCLUSIONS

We demonstrated that expression of genes associated with CD4+ Tconv and Treg could identify patients at risk of ACR. In our post hoc analysis, complementing HEARTBiT with TGS resulted in an improved classification of ACR. Our study suggests that HEARTBiT and TGS may serve as useful tools for further research and test development.

Multiple abnormal peripheral blood gene expression assay results are correlated with subsequent graft loss after kidney transplantation

BACKGROUND

The aim of this study was to correlate peripheral blood gene expression profile (GEP) results during the first post-transplant year with outcomes after kidney transplantation.

METHODS

We conducted a prospective, multicenter observational study of obtaining peripheral blood at five timepoints during the first post-transplant year to perform a GEP assay. The cohort was stratified based on the pattern of the peripheral blood GEP results: Tx-all GEP results normal, 1 Not-TX had one GEP result abnormal and >1 Not-TX two or more abnormal GEP results. We correlated the GEP results with outcomes after transplantation.

RESULTS

We enrolled 240 kidney transplant recipients. The cohort was stratified into the three groups: TX n = 117 (47%), 1 Not-TX n = 59 (25%) and >1 Not-TX n = 64 (27%). Compared to the TX group, the >1 Not-TX group had lower eGFR (p < .001) and more chronic changes on 1-year surveillance biopsy (p = .007). Death censored graft survival showed inferior graft survival in the >1 Not-TX group (p < .001) but not in the 1 Not-TX group. All graft losses in the >1 Not-TX group occurred after 1-year post-transplant.

CONCLUSIONS

We conclude that a pattern of persistently Not-TX GEP assay correlates with inferior graft survival.

The Clinical Utility of Post-Transplant Monitoring of Donor-Specific Antibodies in Stable Renal Transplant Recipients: A Consensus Report With Guideline Statements for Clinical Practice

Solid phase immunoassays improved the detection and determination of the antigen-specificity of donor-specific antibodies (DSA) to human leukocyte antigens (HLA). The widespread use of SPI in kidney transplantation also introduced new clinical dilemmas, such as whether patients should be monitored for DSA pre- or post-transplantation. Pretransplant screening through SPI has become standard practice and DSA are readily determined in case of suspected rejection. However, DSA monitoring in recipients with stable graft function has not been universally established as standard of care. This may be related to uncertainty regarding the clinical utility of DSA monitoring as a screening tool. This consensus report aims to appraise the clinical utility of DSA monitoring in recipients without overt signs of graft dysfunction, using the Wilson & Junger criteria for assessing the validity of a screening practice. To assess the evidence on DSA monitoring, the European Society for Organ Transplantation (ESOT) convened a dedicated workgroup, comprised of experts in transplantation nephrology and immunology, to review relevant literature. Guidelines and statements were developed during a consensus conference by Delphi methodology that took place in person in November 2022 in Prague. The findings and recommendations of the workgroup on subclinical DSA monitoring are presented in this article.

A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study

BACKGROUND

Recurrent graft fibrosis after liver transplantation can threaten both graft and patient survival. Therefore, early detection of fibrosis is essential to avoid disease progression and the need for retransplantation. Non-invasive blood-based biomarkers of fibrosis are limited by moderate accuracy and high cost. We aimed to evaluate the accuracy of machine learning algorithms in detecting graft fibrosis using longitudinal clinical and laboratory data.

METHODS

In this retrospective, longitudinal study, we trained machine learning algorithms, including our novel weighted long short-term memory (LSTM) model, to predict the risk of significant fibrosis using follow-up data from 1893 adults who had a liver transplantation between Feb 1, 1987, and Dec 30, 2019, with at least one liver biopsy post transplantation. Liver biopsy samples with indefinitive fibrosis stage and those from patients with multiple transplantations were excluded. Longitudinal clinical variables were collected from transplantation to the date of last available liver biopsy. Deep learning models were trained on 70% of the patients as the training set and 30% of the patients as the test set. The algorithms were also separately tested on longitudinal data from patients in a subgroup of patients (n=149) who had transient elastography within 1 year before or after the date of liver biopsy. Weighted LSTM model performance for diagnosing significant fibrosis was compared against LSTM, other deep learning models (recurrent neural network and temporal convolutional network), and machine learning models (Random Forest, Support vector machines, Logistic regression, Lasso regression, and Ridge regression) and aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and transient elastography.

FINDINGS

1893 people who had a liver transplantation (1261 [67%] men and 632 [33%] women) with at least one liver biopsy between Jan 1, 1992, and June 30, 2020, were included in the study (591 [31%] cases and 1302 [69%] controls). The median age at liver transplantation was 53·7 years (IQR 47·3-59·0) for cases and 55·3 years (48·0 to 61·2) for controls. The median time interval between transplant and liver biopsy was 21 months (5 to 71). The weighted LSTM model (area under the curve 0·798 [95% CI 0·790 to 0·810]) consistently outperformed other methods, including unweighted LSTM (0·761 [0·750 to 0·769]; p=0·031) Recurrent Neural Network (0·736 [0·721 to 0·744]), Temporal Convolutional Networks (0·700 [0·662 to 0·747], and Random Forest 0·679 [0·652 to 0·707]), FIB-4 (0·650 [0·636 to 0·663]) and APRI (0·682 [0·671 to 0·694]) when diagnosing F2 or worse stage fibrosis. In a subgroup of patients with transient elastography results, weighted LSTM was not significantly better at detecting fibrosis (≥F2; 0·705 [0·687 to 0·724]) than transient elastography (0·685 [0·662 to 0·704]). The top ten variables predictive for significant fibrosis were recipient age, primary indication for transplantation, donor age, and longitudinal data for creatinine, alanine aminotransferase, aspartate aminotransferase, total bilirubin, platelets, white blood cell count, and weight.

INTERPRETATION

Deep learning algorithms, particularly weighted LSTM, outperform other routinely used non-invasive modalities and could help with the earlier diagnosis of graft fibrosis using longitudinal clinical and laboratory variables. The list of most important predictive variables for the development of fibrosis will enable clinicians to modify their management accordingly to prevent onset of graft cirrhosis.

FUNDING

Canadian Institute of Health Research, American Society of Transplantation, Toronto General and Western Hospital Foundation, and Paladin Labs.

A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients

Background

A prediction model for graft survival including donor and recipient characteristics could help clinical decision-making and optimize outcomes. The aim of this study was to develop a risk assessment tool for graft survival based on essential pre-transplantation parameters.

Methods

The data originated from the national Dutch registry (NOTR; Nederlandse OrgaanTransplantatie Registratie). A multivariable binary logistic model was used to predict graft survival, corrected for the transplantation era and time after transplantation. Subsequently, a prediction score was calculated from the β-coefficients. For internal validation, derivation (80%) and validation (20%) cohorts were defined. Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristics curve, Hosmer-Lemeshow test and calibration plots.

Results

In total, 1428 transplantations were performed. Ten-year graft survival was 42% for transplantations before 1990, which has improved to the current value of 92%. Over time, significantly more living and pre-emptive transplantations have been performed and overall donor age has increased (P < .05).The prediction model included 71 829 observations of 554 transplantations between 1990 and 2021. Other variables incorporated in the model were recipient age, re-transplantation, number of human leucocyte antigen (HLA) mismatches and cause of kidney failure. The predictive capacity of this model had AUCs of 0.89, 0.79, 0.76 and 0.74 after 1, 5, 10 and 20 years, respectively (P < .01). Calibration plots showed an excellent fit.

Conclusions

This pediatric pre-transplantation risk assessment tool exhibits good performance for predicting graft survival within the Dutch pediatric population. This model might support decision-making regarding donor selection to optimize graft outcomes.

Trial registration

ClinicalTrials.gov Identifier: NCT05388955.

Physical Frailty and Functional Status in Patients With Advanced Chronic Kidney Disease: A Systematic Review

Background

With an aging population and growing number of patients with chronic kidney disease (CKD), integrating the latest risk factors when deciding on a treatment plan can result in better patient care. Frailty remains a prevalent syndrome in CKD resulting in adverse health outcomes. However, measures of frailty and functional status remain excluded from clinical decision making.

Objective

To examine the degree to which different measures of frailty and functional status are associated with mortality, hospitalization, and other clinical outcomes in patients with advanced CKD.

Design

Systematic review.

Setting

Observation studies including cohort study, case-control study, or cross-sectional study examining frailty and functional status on clinical outcomes. There were no restrictions on type of setting or country of origin.

Patients

Adults with advanced CKD, including both types of dialysis patients.

Measurements

Data including demographic information (e.g., sample size, follow-up time, age, country), assessments of frailty or functional status and their domains, and outcomes including mortality, hospitalization, cardiovascular events, kidney function, and composite outcomes were extracted.

Methods

A search was conducted using databases Medline, Embase, and Cochrane Central Register for Controlled Trials. Studies were included from inception to March 17, 2021. The eligibility of studies was screened by 2 independent reviewers. Data were presented by instrument and clinical outcome. Point estimates and 95% confidence intervals from the fully adjusted statistical model were reported or calculated from the raw data.

Results

A total of 117 unique instruments were found among 140 studies. The median sample size of studies was 319 (interquartile range, 161-893). Most studies focused on incident and chronic dialysis patient populations, with only 15% of studies examining non-dialysis CKD patients. Frailty and lower functional status were associated with an increased risk for adverse clinical outcomes such as mortality and hospitalization. The 5 individual domains of frailty were also found to be associated with poor health outcomes.

Limitations

Meta-analysis could not be performed due to significant heterogeneity between studies and methods used to measure frailty and functional status. Many studies had issues with methodological rigor. Selection bias and the validity of data collection could not be ascertained for some studies.

Conclusion

Frailty and functional status measures should be integrated to help guide clinical care decision making for a comprehensive assessment of risk for adverse outcomes among patients with advanced CKD.

Registration PROSPERO

CRD42016045251.

A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation

BACKGROUND

After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms.

METHODS

Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score.

RESULTS

Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified.

CONCLUSIONS

Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.

Artificial intelligence, machine learning, and deep learning in liver transplantation

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Race-free estimated glomerular filtration rate equation in kidney transplant recipients: development and validation study

OBJECTIVE

To compare the performance of a newly developed race-free kidney recipient specific glomerular filtration rate (GFR) equation with the three current main equations for measuring GFR in kidney transplant recipients.

DESIGN

Development and validation study SETTING: 17 cohorts in Europe, the United States, and Australia (14 transplant centres, three clinical trials).

PARTICIPANTS

15 489 adults (3622 in development cohort (Necker, Saint Louis, and Toulouse hospitals, France), 11 867 in multiple external validation cohorts) who received kidney transplants between 1 January 2000 and 1 January 2021.

MAIN OUTCOME MEASURE

The main outcome measure was GFR, measured according to local practice. Performance of the GFR equations was assessed using P30 (proportion of estimated GFR (eGFR) within 30% of measured GFR (mGFR)) and correct classification (agreement between eGFR and mGFR according to GFR stages). The race-free equation, based on creatinine level, age, and sex, was developed using additive and multiplicative linear regressions, and its performance was compared with the three current main GFR equations: Modification of Diet in Renal Disease (MDRD) equation, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 equation, and race-free CKD-EPI 2021 equation.

RESULTS

The study included 15 489 participants, with 50 464 mGFR and eGFR values. The mean GFR was 53.18 mL/min/1.73m2 (SD 17.23) in the development cohort and 55.90 mL/min/1.73m2 (19.69) in the external validation cohorts. Among the current GFR equations, the race-free CKD-EPI 2021 equation showed the lowest performance compared with the MDRD and CKD-EPI 2009 equations. When race was included in the kidney recipient specific GFR equation, performance did not increase. The race-free kidney recipient specific GFR equation showed significantly improved performance compared with the race-free CKD-EPI 2021 equation and performed well in the external validation cohorts (P30 ranging from 73.0% to 91.3%). The race-free kidney recipient specific GFR equation performed well in several subpopulations of kidney transplant recipients stratified by race (P30 73.0-91.3%), sex (72.7-91.4%), age (70.3-92.0%), body mass index (64.5-100%), donor type (58.5-92.9%), donor age (68.3-94.3%), treatment (78.5-85.2%), creatinine level (72.8-91.3%), GFR measurement method (73.0-91.3%), and timing of GFR measurement post-transplant (72.9-95.5%). An online application was developed that estimates GFR based on recipient's creatinine level, age, and sex (https://transplant-prediction-system.shinyapps.io/eGFR_equation_KTX/).

CONCLUSION

A new race-free kidney recipient specific GFR equation was developed and validated using multiple, large, international cohorts of kidney transplant recipients. The equation showed high accuracy and outperformed the race-free CKD-EPI 2021 equation that was developed in individuals with native kidneys.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05229939.

Kidney transplant in the next decade: Strategies, challenges and vision of the future

Although the results of kidney transplantation (KT) have improved substantially in recent years, a chronic and inexorable loss of grafts mainly due to the death of the patient and chronic dysfunction of the KT, continues to be observed. The objectives, thus, to optimize this situation in the next decade are fundamentally focused on minimizing the rate of kidney graft loss, improving patient survival, increasing the rate of organ procurement and its distribution, promoting research and training in health professionals and the development of scientific registries providing clinical and reliable information that allow us to optimize our clinical practice in the field of KT. With this perspective, this review will deep into: (1) strategies to avoid chronic dysfunction and graft loss in the medium and long term; (2) to prolong patient survival; (3) strategies to increase the donation, maintenance and allocation of organs; (4) promote clinical and basic research and training activity in KT; and (5) the analysis of the results in KT by optimizing and merging scientific registries.

Allograft tissue under the microscope: only the beginning

PURPOSE OF REVIEW

To review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema.

RECENT FINDINGS

Newer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation.

SUMMARY

Banff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.